Overview

Dataset statistics

Number of variables21
Number of observations331262
Missing cells0
Missing cells (%)0.0%
Duplicate rows88200
Duplicate rows (%)26.6%
Total size in memory101.1 MiB
Average record size in memory320.0 B

Variable types

Numeric14
Categorical4
Unsupported3

Warnings

RevisionNumber has constant value "1" Constant
OrderQuantity has constant value "1" Constant
Dataset has 88200 (26.6%) duplicate rows Duplicates
SalesOrderNumber has a high cardinality: 27659 distinct values High cardinality
SaleTypeKey is highly correlated with SalesOrderLineNumberHigh correlation
SalesOrderLineNumber is highly correlated with SaleTypeKeyHigh correlation
UnitPrice is highly correlated with ProductStandardCost and 5 other fieldsHigh correlation
ProductStandardCost is highly correlated with UnitPrice and 5 other fieldsHigh correlation
TotalProductCost is highly correlated with UnitPrice and 5 other fieldsHigh correlation
SalesAmount is highly correlated with UnitPrice and 5 other fieldsHigh correlation
TaxAmt is highly correlated with UnitPrice and 5 other fieldsHigh correlation
Freight is highly correlated with UnitPrice and 5 other fieldsHigh correlation
ExtendedAmount is highly correlated with UnitPrice and 5 other fieldsHigh correlation
RevisionNumber is highly correlated with OrderQuantity and 1 other fieldsHigh correlation
OrderQuantity is highly correlated with RevisionNumber and 1 other fieldsHigh correlation
PromotionKey is highly correlated with RevisionNumber and 1 other fieldsHigh correlation
OrderDate is an unsupported type, check if it needs cleaning or further analysis Unsupported
DueDate is an unsupported type, check if it needs cleaning or further analysis Unsupported
ShipDate is an unsupported type, check if it needs cleaning or further analysis Unsupported
UnitPriceDiscountPct has 290427 (87.7%) zeros Zeros

Reproduction

Analysis started2021-03-08 02:59:28.280623
Analysis finished2021-03-08 03:00:31.756630
Duration1 minute and 3.48 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

DateKey
Real number (ℝ≥0)

Distinct1123
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20159803.01
Minimum20131229
Maximum20170128
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:31.818831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20131229
5-th percentile20150505
Q120160404
median20160714
Q320161018
95-th percentile20161224
Maximum20170128
Range38899
Interquartile range (IQR)614

Descriptive statistics

Standard deviation4624.07144
Coefficient of variation (CV)0.0002293708643
Kurtosis8.718159645
Mean20159803.01
Median Absolute Deviation (MAD)307
Skewness-2.460296333
Sum6.678176664 × 1012
Variance21382036.68
MonotocityNot monotonic
2021-03-07T22:00:31.943841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201612121421
 
0.4%
201610011358
 
0.4%
201606051322
 
0.4%
201608191311
 
0.4%
201612091308
 
0.4%
201612051242
 
0.4%
201610171216
 
0.4%
201612261213
 
0.4%
201612191212
 
0.4%
201612151211
 
0.4%
Other values (1113)318448
96.1%
ValueCountFrequency (%)
2013122925
< 0.1%
2013123020
< 0.1%
2013123125
< 0.1%
2014010110
 
< 0.1%
2014010225
< 0.1%
2014010320
< 0.1%
2014010415
< 0.1%
2014010515
< 0.1%
2014010630
< 0.1%
2014010715
< 0.1%
ValueCountFrequency (%)
20170128527
0.2%
20170127342
0.1%
20170126387
0.1%
20170125452
0.1%
20170124355
0.1%
20170123415
0.1%
20170122307
0.1%
20170121468
0.1%
20170120389
0.1%
20170119413
0.1%

ProductKey
Real number (ℝ≥0)

Distinct606
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325.3774233
Minimum1
Maximum606
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:32.084487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile57
Q1216
median324
Q3417
95-th percentile572
Maximum606
Range605
Interquartile range (IQR)201

Descriptive statistics

Standard deviation154.0875158
Coefficient of variation (CV)0.4735654803
Kurtosis-0.6406707325
Mean325.3774233
Median Absolute Deviation (MAD)96
Skewness-0.3000840847
Sum107785176
Variance23742.96251
MonotocityNot monotonic
2021-03-07T22:00:32.178230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3038488
 
2.6%
4008488
 
2.6%
4036382
 
1.9%
3066382
 
1.9%
4156190
 
1.9%
3186190
 
1.9%
3194752
 
1.4%
4164752
 
1.4%
3114523
 
1.4%
5574244
 
1.3%
Other values (596)270871
81.8%
ValueCountFrequency (%)
12230
0.7%
22085
0.6%
32125
0.6%
42190
0.7%
5429
 
0.1%
6442
 
0.1%
7452
 
0.1%
8413
 
0.1%
9336
 
0.1%
10281
 
0.1%
ValueCountFrequency (%)
606334
0.1%
605232
0.1%
604216
0.1%
603235
0.1%
602246
0.1%
60188
 
< 0.1%
600106
 
< 0.1%
59997
 
< 0.1%
598147
< 0.1%
597160
< 0.1%

CustomerKey
Real number (ℝ≥0)

Distinct18484
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18860.50353
Minimum11000
Maximum29483
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:32.305264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11000
5-th percentile11410.05
Q114023
median18177
Q323447
95-th percentile28100
Maximum29483
Range18483
Interquartile range (IQR)9424

Descriptive statistics

Standard deviation5428.760474
Coefficient of variation (CV)0.2878375153
Kurtosis-1.163990054
Mean18860.50353
Median Absolute Deviation (MAD)4601
Skewness0.276155324
Sum6247768121
Variance29471440.28
MonotocityNot monotonic
2021-03-07T22:00:32.416029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11300383
 
0.1%
11185372
 
0.1%
11277365
 
0.1%
11262356
 
0.1%
11287352
 
0.1%
11091335
 
0.1%
11176335
 
0.1%
11331334
 
0.1%
11276321
 
0.1%
11566318
 
0.1%
Other values (18474)327791
99.0%
ValueCountFrequency (%)
1100043
< 0.1%
1100159
< 0.1%
1100220
 
< 0.1%
1100350
< 0.1%
1100429
< 0.1%
1100534
< 0.1%
1100628
< 0.1%
1100741
< 0.1%
1100837
< 0.1%
1100926
< 0.1%
ValueCountFrequency (%)
294835
 
< 0.1%
294825
 
< 0.1%
294815
 
< 0.1%
2948026
< 0.1%
294795
 
< 0.1%
2947818
< 0.1%
2947714
< 0.1%
294765
 
< 0.1%
294755
 
< 0.1%
294745
 
< 0.1%

PromotionKey
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
1
319138 
2
 
11931
13
 
115
14
 
78

Length

Max length2
Median length1
Mean length1.00058262
Min length1

Characters and Unicode

Total characters331455
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1319138
96.3%
211931
 
3.6%
13115
 
< 0.1%
1478
 
< 0.1%
2021-03-07T22:00:32.650403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T22:00:32.744475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1319138
96.3%
211931
 
3.6%
13115
 
< 0.1%
1478
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1319331
96.3%
211931
 
3.6%
3115
 
< 0.1%
478
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number331455
100.0%

Most frequent character per category

ValueCountFrequency (%)
1319331
96.3%
211931
 
3.6%
3115
 
< 0.1%
478
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common331455
100.0%

Most frequent character per script

ValueCountFrequency (%)
1319331
96.3%
211931
 
3.6%
3115
 
< 0.1%
478
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII331455
100.0%

Most frequent character per block

ValueCountFrequency (%)
1319331
96.3%
211931
 
3.6%
3115
 
< 0.1%
478
 
< 0.1%

CurrencyKey
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.66434122
Minimum6
Maximum100
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:32.822292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q119
median100
Q3100
95-th percentile100
Maximum100
Range94
Interquartile range (IQR)81

Descriptive statistics

Standard deviation42.20774105
Coefficient of variation (CV)0.6058729662
Kurtosis-1.463107111
Mean69.66434122
Median Absolute Deviation (MAD)0
Skewness-0.7107491323
Sum23077149
Variance1781.493405
MonotocityNot monotonic
2021-03-07T22:00:32.900419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
100182918
55.2%
671534
 
21.6%
1939590
 
12.0%
9836545
 
11.0%
29380
 
0.1%
39295
 
0.1%
ValueCountFrequency (%)
671534
 
21.6%
1939590
 
12.0%
29380
 
0.1%
39295
 
0.1%
9836545
 
11.0%
100182918
55.2%
ValueCountFrequency (%)
100182918
55.2%
9836545
 
11.0%
39295
 
0.1%
29380
 
0.1%
1939590
 
12.0%
671534
 
21.6%

OrderDate
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size12.6 MiB

DueDate
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size12.6 MiB

ShipDate
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size12.6 MiB

SaleTypeKey
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.853403047
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:32.978560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9982626371
Coefficient of variation (CV)0.5386106594
Kurtosis0.8058291079
Mean1.853403047
Median Absolute Deviation (MAD)1
Skewness1.105900491
Sum613962
Variance0.9965282926
MonotocityNot monotonic
2021-03-07T22:00:33.065172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1156023
47.1%
298098
29.6%
352460
 
15.8%
419846
 
6.0%
54129
 
1.2%
6620
 
0.2%
774
 
< 0.1%
812
 
< 0.1%
ValueCountFrequency (%)
1156023
47.1%
298098
29.6%
352460
 
15.8%
419846
 
6.0%
54129
 
1.2%
6620
 
0.2%
774
 
< 0.1%
812
 
< 0.1%
ValueCountFrequency (%)
812
 
< 0.1%
774
 
< 0.1%
6620
 
0.2%
54129
 
1.2%
419846
 
6.0%
352460
 
15.8%
298098
29.6%
1156023
47.1%

SalesOrderNumber
Categorical

HIGH CARDINALITY

Distinct27659
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
SO70714
 
45
SO58845
 
43
SO72656
 
42
SO54784
 
42
SO54042
 
42
Other values (27654)
331048 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters2318834
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSO70513
2nd rowSO70517
3rd rowSO70586
4th rowSO70661
5th rowSO70647
ValueCountFrequency (%)
SO7071445
 
< 0.1%
SO5884543
 
< 0.1%
SO7265642
 
< 0.1%
SO5478442
 
< 0.1%
SO5404242
 
< 0.1%
SO5155541
 
< 0.1%
SO6404241
 
< 0.1%
SO6141240
 
< 0.1%
SO6023339
 
< 0.1%
SO6298439
 
< 0.1%
Other values (27649)330848
99.9%
2021-03-07T22:00:33.347590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
so7071445
 
< 0.1%
so5884543
 
< 0.1%
so5478442
 
< 0.1%
so7265642
 
< 0.1%
so5404242
 
< 0.1%
so5155541
 
< 0.1%
so6404241
 
< 0.1%
so6141240
 
< 0.1%
so7196139
 
< 0.1%
so5857239
 
< 0.1%
Other values (27649)330848
99.9%

Most occurring characters

ValueCountFrequency (%)
S331262
14.3%
O331262
14.3%
6259047
11.2%
5241674
10.4%
7193922
8.4%
4168104
7.2%
2137567
5.9%
3136799
5.9%
1131361
 
5.7%
0130485
 
5.6%
Other values (2)257351
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1656310
71.4%
Uppercase Letter662524
 
28.6%

Most frequent character per category

ValueCountFrequency (%)
6259047
15.6%
5241674
14.6%
7193922
11.7%
4168104
10.1%
2137567
8.3%
3136799
8.3%
1131361
7.9%
0130485
7.9%
8129506
7.8%
9127845
7.7%
ValueCountFrequency (%)
S331262
50.0%
O331262
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1656310
71.4%
Latin662524
 
28.6%

Most frequent character per script

ValueCountFrequency (%)
6259047
15.6%
5241674
14.6%
7193922
11.7%
4168104
10.1%
2137567
8.3%
3136799
8.3%
1131361
7.9%
0130485
7.9%
8129506
7.8%
9127845
7.7%
ValueCountFrequency (%)
S331262
50.0%
O331262
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2318834
100.0%

Most frequent character per block

ValueCountFrequency (%)
S331262
14.3%
O331262
14.3%
6259047
11.2%
5241674
10.4%
7193922
8.4%
4168104
7.2%
2137567
5.9%
3136799
5.9%
1131361
 
5.7%
0130485
 
5.6%
Other values (2)257351
11.1%

SalesOrderLineNumber
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.853403047
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:33.428205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9982626371
Coefficient of variation (CV)0.5386106594
Kurtosis0.8058291079
Mean1.853403047
Median Absolute Deviation (MAD)1
Skewness1.105900491
Sum613962
Variance0.9965282926
MonotocityNot monotonic
2021-03-07T22:00:33.507492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1156023
47.1%
298098
29.6%
352460
 
15.8%
419846
 
6.0%
54129
 
1.2%
6620
 
0.2%
774
 
< 0.1%
812
 
< 0.1%
ValueCountFrequency (%)
1156023
47.1%
298098
29.6%
352460
 
15.8%
419846
 
6.0%
54129
 
1.2%
6620
 
0.2%
774
 
< 0.1%
812
 
< 0.1%
ValueCountFrequency (%)
812
 
< 0.1%
774
 
< 0.1%
6620
 
0.2%
54129
 
1.2%
419846
 
6.0%
352460
 
15.8%
298098
29.6%
1156023
47.1%

RevisionNumber
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
1
331262 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters331262
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1331262
100.0%
2021-03-07T22:00:33.710631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T22:00:33.804389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1331262
100.0%

Most occurring characters

ValueCountFrequency (%)
1331262
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number331262
100.0%

Most frequent character per category

ValueCountFrequency (%)
1331262
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common331262
100.0%

Most frequent character per script

ValueCountFrequency (%)
1331262
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII331262
100.0%

Most frequent character per block

ValueCountFrequency (%)
1331262
100.0%

OrderQuantity
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
1
331262 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters331262
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
1331262
100.0%
2021-03-07T22:00:33.976275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-07T22:00:34.054386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1331262
100.0%

Most occurring characters

ValueCountFrequency (%)
1331262
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number331262
100.0%

Most frequent character per category

ValueCountFrequency (%)
1331262
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common331262
100.0%

Most frequent character per script

ValueCountFrequency (%)
1331262
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII331262
100.0%

Most frequent character per block

ValueCountFrequency (%)
1331262
100.0%

UnitPrice
Real number (ℝ≥0)

HIGH CORRELATION

Distinct133
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.3153472
Minimum12.99
Maximum2899.99
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:34.116908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum12.99
5-th percentile67.4
Q1219
median369
Q3699
95-th percentile1299
Maximum2899.99
Range2887
Interquartile range (IQR)480

Descriptive statistics

Standard deviation408.9683636
Coefficient of variation (CV)0.8141665706
Kurtosis5.756004151
Mean502.3153472
Median Absolute Deviation (MAD)230
Skewness1.894929663
Sum166397986.5
Variance167255.1224
MonotocityNot monotonic
2021-03-07T22:00:34.227594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69926363
 
8.0%
75815501
 
4.7%
59915059
 
4.5%
36914837
 
4.5%
86914503
 
4.4%
32612374
 
3.7%
2799134
 
2.8%
2999133
 
2.8%
9999029
 
2.7%
149.998696
 
2.6%
Other values (123)196633
59.4%
ValueCountFrequency (%)
12.994315
1.3%
14.522125
0.6%
21.573513
1.1%
25.69620
 
0.2%
37.95582
 
0.2%
40.551458
 
0.4%
47.95463
 
0.1%
49502
 
0.2%
56.980
 
< 0.1%
59649
 
0.2%
ValueCountFrequency (%)
2899.99267
 
0.1%
24992498
 
0.8%
22951708
 
0.5%
1592.25186
1.6%
12997935
2.4%
1199117
 
< 0.1%
1184.97195
 
0.1%
1109688
 
0.2%
1099.995064
1.5%
1099376
 
0.1%

UnitPriceDiscountPct
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.018693662
Minimum0
Maximum22.5
Zeros290427
Zeros (%)87.7%
Memory size2.5 MiB
2021-03-07T22:00:34.338123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18
Maximum22.5
Range22.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.439369563
Coefficient of variation (CV)2.694499747
Kurtosis3.832743589
Mean2.018693662
Median Absolute Deviation (MAD)0
Skewness2.381278229
Sum668716.5
Variance29.58674124
MonotocityNot monotonic
2021-03-07T22:00:34.416258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0290427
87.7%
1820910
 
6.3%
1414976
 
4.5%
19.52900
 
0.9%
12.51998
 
0.6%
22.551
 
< 0.1%
ValueCountFrequency (%)
0290427
87.7%
12.51998
 
0.6%
1414976
 
4.5%
1820910
 
6.3%
19.52900
 
0.9%
22.551
 
< 0.1%
ValueCountFrequency (%)
22.551
 
< 0.1%
19.52900
 
0.9%
1820910
 
6.3%
1414976
 
4.5%
12.51998
 
0.6%
0290427
87.7%

ProductStandardCost
Real number (ℝ≥0)

HIGH CORRELATION

Distinct149
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.5625904
Minimum6.62
Maximum960.82
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:34.510031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6.62
5-th percentile34.36
Q191.95
median169.69
Q3321.05
95-th percentile459.4
Maximum960.82
Range954.2
Interquartile range (IQR)229.1

Descriptive statistics

Standard deviation142.5715625
Coefficient of variation (CV)0.6835912532
Kurtosis2.908754623
Mean208.5625904
Median Absolute Deviation (MAD)105.77
Skewness1.226892581
Sum69088860.82
Variance20326.65045
MonotocityNot monotonic
2021-03-07T22:00:34.635037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321.4426363
 
8.0%
348.5815501
 
4.7%
275.4615059
 
4.5%
287.9214503
 
4.4%
166.212374
 
3.7%
169.6912145
 
3.7%
49.698696
 
2.6%
430.387935
 
2.4%
142.247441
 
2.2%
61.167283
 
2.2%
Other values (139)203962
61.6%
ValueCountFrequency (%)
6.624315
1.3%
7.42125
0.6%
113513
1.1%
13.1620
 
0.2%
17.45582
 
0.2%
18.651458
 
0.4%
22.05463
 
0.1%
22.86464
 
0.1%
24.98502
 
0.2%
29.0180
 
< 0.1%
ValueCountFrequency (%)
960.82267
 
0.1%
827.972498
 
0.8%
760.381708
 
0.5%
527.535186
1.6%
505.855064
1.5%
459.41971
 
0.6%
444.69397
 
0.1%
430.387935
2.4%
404.63136
 
< 0.1%
397.25117
 
< 0.1%

TotalProductCost
Real number (ℝ≥0)

HIGH CORRELATION

Distinct149
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.5625904
Minimum6.62
Maximum960.82
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:34.746253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6.62
5-th percentile34.36
Q191.95
median169.69
Q3321.05
95-th percentile459.4
Maximum960.82
Range954.2
Interquartile range (IQR)229.1

Descriptive statistics

Standard deviation142.5715625
Coefficient of variation (CV)0.6835912532
Kurtosis2.908754623
Mean208.5625904
Median Absolute Deviation (MAD)105.77
Skewness1.226892581
Sum69088860.82
Variance20326.65045
MonotocityNot monotonic
2021-03-07T22:00:34.872677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
321.4426363
 
8.0%
348.5815501
 
4.7%
275.4615059
 
4.5%
287.9214503
 
4.4%
166.212374
 
3.7%
169.6912145
 
3.7%
49.698696
 
2.6%
430.387935
 
2.4%
142.247441
 
2.2%
61.167283
 
2.2%
Other values (139)203962
61.6%
ValueCountFrequency (%)
6.624315
1.3%
7.42125
0.6%
113513
1.1%
13.1620
 
0.2%
17.45582
 
0.2%
18.651458
 
0.4%
22.05463
 
0.1%
22.86464
 
0.1%
24.98502
 
0.2%
29.0180
 
< 0.1%
ValueCountFrequency (%)
960.82267
 
0.1%
827.972498
 
0.8%
760.381708
 
0.5%
527.535186
1.6%
505.855064
1.5%
459.41971
 
0.6%
444.69397
 
0.1%
430.387935
2.4%
404.63136
 
< 0.1%
397.25117
 
< 0.1%

SalesAmount
Real number (ℝ≥0)

HIGH CORRELATION

Distinct250
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean491.1485504
Minimum10.6518
Maximum2899.99
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:34.982073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10.6518
5-th percentile67.4
Q1204.9918
median369
Q3699
95-th percentile1099.99
Maximum2899.99
Range2889.3382
Interquartile range (IQR)494.0082

Descriptive statistics

Standard deviation402.5377754
Coefficient of variation (CV)0.8195845738
Kurtosis5.967969403
Mean491.1485504
Median Absolute Deviation (MAD)230
Skewness1.926724007
Sum162698851.1
Variance162036.6606
MonotocityNot monotonic
2021-03-07T22:00:35.091444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69923660
 
7.1%
36914187
 
4.3%
59913573
 
4.1%
75813385
 
4.0%
86912831
 
3.9%
32611368
 
3.4%
9998404
 
2.5%
2798115
 
2.4%
2998061
 
2.4%
149.997482
 
2.3%
Other values (240)210196
63.5%
ValueCountFrequency (%)
10.6518259
 
0.1%
11.9064125
 
< 0.1%
12.994056
1.2%
14.522000
0.6%
17.6874377
 
0.1%
21.065893
 
< 0.1%
21.573136
0.9%
25.69527
 
0.2%
31.11981
 
< 0.1%
33.251168
 
0.1%
ValueCountFrequency (%)
2899.99198
 
0.1%
24992380
 
0.7%
2377.991869
 
< 0.1%
22951534
 
0.5%
2049.18118
 
< 0.1%
1881.9174
 
0.1%
1592.24714
1.4%
1305.604472
 
0.1%
12996002
1.8%
119987
 
< 0.1%

TaxAmt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct133
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.23153472
Minimum1.299
Maximum289.999
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:35.216463image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.299
5-th percentile6.74
Q121.9
median36.9
Q369.9
95-th percentile129.9
Maximum289.999
Range288.7
Interquartile range (IQR)48

Descriptive statistics

Standard deviation40.89683636
Coefficient of variation (CV)0.8141665706
Kurtosis5.756004151
Mean50.23153472
Median Absolute Deviation (MAD)23
Skewness1.894929663
Sum16639798.65
Variance1672.551224
MonotocityNot monotonic
2021-03-07T22:00:35.325851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.926363
 
8.0%
75.815501
 
4.7%
59.915059
 
4.5%
36.914837
 
4.5%
86.914503
 
4.4%
32.612374
 
3.7%
27.99134
 
2.8%
29.99133
 
2.8%
99.99029
 
2.7%
14.9998696
 
2.6%
Other values (123)196633
59.4%
ValueCountFrequency (%)
1.2994315
1.3%
1.4522125
0.6%
2.1573513
1.1%
2.569620
 
0.2%
3.795582
 
0.2%
4.0551458
 
0.4%
4.795463
 
0.1%
4.9502
 
0.2%
5.6980
 
< 0.1%
5.9649
 
0.2%
ValueCountFrequency (%)
289.999267
 
0.1%
249.92498
 
0.8%
229.51708
 
0.5%
159.225186
1.6%
129.97935
2.4%
119.9117
 
< 0.1%
118.497195
 
0.1%
110.9688
 
0.2%
109.9995064
1.5%
109.9376
 
0.1%

Freight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct133
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.5093764
Minimum2.8578
Maximum637.9978
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:35.450841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.8578
5-th percentile14.828
Q148.18
median81.18
Q3153.78
95-th percentile285.78
Maximum637.9978
Range635.14
Interquartile range (IQR)105.6

Descriptive statistics

Standard deviation89.97303999
Coefficient of variation (CV)0.8141665706
Kurtosis5.756004151
Mean110.5093764
Median Absolute Deviation (MAD)50.6
Skewness1.894929663
Sum36607557.04
Variance8095.147924
MonotocityNot monotonic
2021-03-07T22:00:35.560240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
153.7826363
 
8.0%
166.7615501
 
4.7%
131.7815059
 
4.5%
81.1814837
 
4.5%
191.1814503
 
4.4%
71.7212374
 
3.7%
61.389134
 
2.8%
65.789133
 
2.8%
219.789029
 
2.7%
32.99788696
 
2.6%
Other values (123)196633
59.4%
ValueCountFrequency (%)
2.85784315
1.3%
3.19442125
0.6%
4.74543513
1.1%
5.6518620
 
0.2%
8.349582
 
0.2%
8.9211458
 
0.4%
10.549463
 
0.1%
10.78502
 
0.2%
12.51880
 
< 0.1%
12.98649
 
0.2%
ValueCountFrequency (%)
637.9978267
 
0.1%
549.782498
 
0.8%
504.91708
 
0.5%
350.2845186
1.6%
285.787935
2.4%
263.78117
 
< 0.1%
260.6934195
 
0.1%
243.98688
 
0.2%
241.99785064
1.5%
241.78376
 
0.1%

ExtendedAmount
Real number (ℝ≥0)

HIGH CORRELATION

Distinct133
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean552.5468819
Minimum14.289
Maximum3189.989
Zeros0
Zeros (%)0.0%
Memory size2.5 MiB
2021-03-07T22:00:35.685232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum14.289
5-th percentile74.14
Q1240.9
median405.9
Q3768.9
95-th percentile1428.9
Maximum3189.989
Range3175.7
Interquartile range (IQR)528

Descriptive statistics

Standard deviation449.8651999
Coefficient of variation (CV)0.8141665706
Kurtosis5.756004151
Mean552.5468819
Median Absolute Deviation (MAD)253
Skewness1.894929663
Sum183037785.2
Variance202378.6981
MonotocityNot monotonic
2021-03-07T22:00:35.779006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
768.926363
 
8.0%
833.815501
 
4.7%
658.915059
 
4.5%
405.914837
 
4.5%
955.914503
 
4.4%
358.612374
 
3.7%
306.99134
 
2.8%
328.99133
 
2.8%
1098.99029
 
2.7%
164.9898696
 
2.6%
Other values (123)196633
59.4%
ValueCountFrequency (%)
14.2894315
1.3%
15.9722125
0.6%
23.7273513
1.1%
28.259620
 
0.2%
41.745582
 
0.2%
44.6051458
 
0.4%
52.745463
 
0.1%
53.9502
 
0.2%
62.5980
 
< 0.1%
64.9649
 
0.2%
ValueCountFrequency (%)
3189.989267
 
0.1%
2748.92498
 
0.8%
2524.51708
 
0.5%
1751.425186
1.6%
1428.97935
2.4%
1318.9117
 
< 0.1%
1303.467195
 
0.1%
1219.9688
 
0.2%
1209.9895064
1.5%
1208.9376
 
0.1%

Interactions

2021-03-07T21:59:47.820699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:48.055103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:48.321761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:48.627447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:48.868484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:49.102860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:49.337248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:49.587278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:49.822081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:50.072090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:50.337740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:50.587749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:50.837774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:51.084804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:51.350447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:51.584839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:51.803602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:52.022374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:52.258706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:52.494231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:52.728637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:52.963025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:53.196874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:53.415138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:53.649551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:53.868545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:54.087069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:54.321460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:54.524598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:54.743350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:54.962129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:55.196505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:55.399647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:55.634048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:55.887426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:56.121407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:56.353830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:56.593298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:56.820445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:57.166582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:57.386486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:57.605509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:57.810465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:58.013606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:58.263621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:58.482397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:58.685536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:58.904298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:59.123065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:59.341831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:59.560596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T21:59:59.795295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T22:00:00.029364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T22:00:00.272355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T22:00:00.512940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T22:00:00.747326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T22:00:00.966102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-07T22:00:01.226417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-03-07T22:00:35.935264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-07T22:00:36.226332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-07T22:00:36.476365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-07T22:00:36.726367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-07T22:00:37.232124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-07T22:00:29.647534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-07T22:00:30.381971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateKeyProductKeyCustomerKeyPromotionKeyCurrencyKeyOrderDateDueDateShipDateSaleTypeKeySalesOrderNumberSalesOrderLineNumberRevisionNumberOrderQuantityUnitPriceUnitPriceDiscountPctProductStandardCostTotalProductCostSalesAmountTaxAmtFreightExtendedAmount
020161110802158811002016-11-102016-11-222016-11-171SO7051311140.550.018.6518.6540.5504.0558.92144.605
12016111080248351982016-11-102016-11-222016-11-171SO7051711140.550.018.6518.6540.5504.0558.92144.605
220161111802179811002016-11-112016-11-232016-11-181SO7058611140.550.018.6518.6540.5504.0558.92144.605
320161112802160711002016-11-122016-11-242016-11-191SO7066111140.550.018.6518.6540.5504.0558.92144.605
4201611128026764162016-11-122016-11-242016-11-191SO7064711140.550.018.6518.6540.5504.0558.92144.605
520161113802181211002016-11-132016-11-252016-11-201SO7073911140.550.018.6518.6540.5504.0558.92144.605
6201611148017927162016-11-142016-11-262016-11-211SO7080111140.5518.018.6518.6533.2514.0558.92144.605
720161114802088811002016-11-142016-11-262016-11-211SO7081711140.550.018.6518.6540.5504.0558.92144.605
820161114802141011002016-11-142016-11-262016-11-211SO7081511140.550.018.6518.6540.5504.0558.92144.605
9201611168027586162016-11-162016-11-282016-11-231SO7091911140.550.018.6518.6540.5504.0558.92144.605

Last rows

DateKeyProductKeyCustomerKeyPromotionKeyCurrencyKeyOrderDateDueDateShipDateSaleTypeKeySalesOrderNumberSalesOrderLineNumberRevisionNumberOrderQuantityUnitPriceUnitPriceDiscountPctProductStandardCostTotalProductCostSalesAmountTaxAmtFreightExtendedAmount
331252201510151911586211002015-10-152015-10-272015-10-221SO5007311166.00.033.6533.6566.06.614.5272.6
3312532015102319126919162015-10-232015-11-042015-10-301SO5015011166.00.033.6533.6566.06.614.5272.6
3312542015110919127370162015-11-092015-11-212015-11-161SO5047011166.00.033.6533.6566.06.614.5272.6
331255201511101911589311002015-11-102015-11-222015-11-171SO5047811166.00.033.6533.6566.06.614.5272.6
331256201511241911594111002015-11-242015-12-062015-12-011SO5061011166.00.033.6533.6566.06.614.5272.6
331257201511271912094511002015-11-272015-12-092015-12-041SO5064711166.00.033.6533.6566.06.614.5272.6
33125820151128191216691982015-11-282015-12-102015-12-051SO5076511166.00.033.6533.6566.06.614.5272.6
331259201512091911597611002015-12-092015-12-212015-12-161SO5089111166.00.033.6533.6566.06.614.5272.6
3312602015121819127472162015-12-182015-12-302015-12-251SO5099311166.00.033.6533.6566.06.614.5272.6
331261201512261911599211002015-12-262016-01-072016-01-021SO5106711166.00.033.6533.6566.06.614.5272.6

Duplicate rows

Most frequent

DateKeyProductKeyCustomerKeyPromotionKeyCurrencyKeySaleTypeKeySalesOrderNumberSalesOrderLineNumberRevisionNumberOrderQuantityUnitPriceUnitPriceDiscountPctProductStandardCostTotalProductCostSalesAmountTaxAmtFreightExtendedAmountcount
0201412292741546011001SO46677111399.00.0183.49183.49399.039.987.78438.92
12014122927421193161SO46680111399.00.0183.49183.49399.039.987.78438.92
2201412292761306211001SO46679111529.00.0243.27243.27529.052.9116.38581.92
3201412292881804711001SO46676111199.00.0101.46101.46199.019.943.78218.92
4201412293711546011001SO46677111599.00.0275.46275.46599.059.9131.78658.92
52014122937121193161SO46680111599.00.0275.46275.46599.059.9131.78658.92
6201412293731306211001SO46679111326.00.0166.20166.20326.032.671.72358.62
7201412293851804711001SO46676111326.00.0166.20166.20326.032.671.72358.62
8201412302801294711001SO46685111329.00.0167.73167.73329.032.972.38361.92
9201412302841812511001SO46684111489.00.0224.87224.87489.048.9107.58537.92